Bioluminescence tomography

BioPhotonicsMar 2008
Bioluminescence imaging has become increasingly important for biomedical research and drug development with mouse models of human diseases. However, its spatial resolution remains limited by the optical heterogeneity of mouse tissue. A novel method called bioluminescence tomography corrects for this heterogeneity using a method similar to CT, producing high-resolution three-dimensional image reconstructions that enable 3-D localization and quantitation of bioluminescence.

In a review, Ge Wang and his collaborators from Virginia Polytechnic Institute and State University in Blacksburg describe the development of this imaging modality. They detail the image reconstruction process, from modeling light-tissue interactions and anatomy to accounting for attenuation. They state that they used the diffusion approximation model to describe bioluminescence through tissue and micro-CT or micro-MRI for geometric modeling. They used finite-element analysis to facilitate assignment of bioluminescence signals from around a mouse body into corresponding spectral bands.

The investigators also describe image unmixing for multispectral, multiprobe bioluminescence tomography. For this purpose, they used mathematical matrices to represent each pixel as a combination of multiple spectra from various bioluminescent probes and statistical analysis methods to unravel signal overlap from probes that are spectrally close together. They also developed methods of exploiting temperature effects on the bioluminescence signal.

They state that they have developed two generations of bioluminescence tomography methods and equipment, achieving 1- to 3-mm accuracy in source localization and 10 to 30 percent error in source power estimation. They describe their tissue-phantom experiment and in vivo work with a mouse model of metastatic prostate cancer.

They are developing a multiview and multispectral imaging system that will enable imaging with probes of multiple colors. They describe a prototypical design and say that the finished product will minimize signal decay, improve the signal-to-noise ratio and significantly increase system throughput. (Frontiers in Bioscience, Jan. 1, 2008, pp. 1281-1293.)

In a vision system, the linear dimensions (X and Y) of the field of view, as measured in the image plane, divided by the number of pixels in the X and Y dimensions of the system's imaging array or image digitizer, expressed in mils or inches per pixel.